Towards indigenous community-led monitoring of fish in the oil sands region of Canada: Lessons at the intersection of cultural consensus and fish science
Bibliographic record
Abstract
In the Oil Sands Regions of Alberta, Canada, Indigenous reassertion of rights and responsibilities has lead to a renewed leadership in monitoring the effects of industries on various environment receptors. This study, conducted with Cold Lake First Nations, Alberta (CLFN), sought to explore local concerns regarding fish consumption safety and population health in response to multiple anthropogenic stressors focusing upon oil extraction. We undertook this work using a novel research design comprised of two distinct approaches including a participatory fish health and toxicology study and a cultural consensus survey of CLFN members. The cultural consensus study assessed similarities and differences in knowledge and perceptions of CLFN members. The fish toxicology and health research involved implementing a co-designed protocol to collect and sample fish for toxicants and overall population health using scientific indicators. We discuss the results of each study as well as the tangible application of our work in achieving a Multiple Evidence Base approach. Our work highlights that complementarities between our studies as part of a negotiated research process can form a single cohesive narrative to better inform fisheries management while respecting community knowledge, culture and rights to access land, water and country foods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".